@spatialjlo

ai product partnerships @nvidia | prev head of partnerships @GroqInc | 🎤 sings macy gray at karaoke

 New York, NY
Joined March 2015
  • Tweets1.7K
  • Following529
  • Followers1.7K
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Jacob Loewenstein retweeted
Aditya Grover
@adityagrover_5h
Over the past two years of building @_inception_ai, one question has come up for me more than any other across countless conversations: Does the diffusion advantage scale? Today, we're answering that question with Mercury 3, our most capable diffusion LLM yet. Mercury 3 surpasses GPT-6-Luna and Claude 5.5 Haiku while delivering blazing-fast inference at 1000+ tokens/sec. More importantly, the Mercury factory is up and running: Compared to Mercury 1, Mercury 3 is 40x larger and 3x more intelligent, all while maintaining the same industry-leading speed. The diffusion advantage doesn't just scale. It compounds.
Stefano Ermon
@StefanoErmon6h
1/4 Today, we’re releasing Mercury 3. Mercury 3 is by far our most powerful model and the most intelligent diffusion LLM ever released. It outperforms GPT-6 Luna and Haiku 5.5 on quality while running at 1000+ tok/sec.
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Just seeing the improvement in such a short time from 2.5 to 3 is pretty mind blowing. like 50% improvement in intelligence in only a few weeks 🚀
Stefano Ermon
@StefanoErmon6h
Replying to @StefanoErmon
2/4 Mercury 3 demonstrates that text diffusion scales. At over 40x the size of Mercury 1, it’s vastly more intelligent while remaining just as fast. This is just the beginning: diffusion opens a new path to scaling AI, where intelligence can grow without a proportional increase in latency.
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dLLMs remain one of the coolest and most slept on architecturally developers. Big leap fwd in intelligence and you can't get this kind of speed anywhere else w/o hardware acceleration!!!
Stefano Ermon
@StefanoErmon6h
1/4 Today, we’re releasing Mercury 3. Mercury 3 is by far our most powerful model and the most intelligent diffusion LLM ever released. It outperforms GPT-6 Luna and Haiku 5.5 on quality while running at 1000+ tok/sec.
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Jacob Loewenstein retweeted
Misha Laskin
@MishaLaskinOct 16
Beam weights are out today (Apache 2.0 license). We integrated Beam across 40+ different partners. And are also open sourcing Mirror CLI - the harness Beam was trained against. Happy building.
Reflection
@reflection_aiOct 16
Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source harness we built and trained Beam on: Mirror CLI Weights and model card on Hugging Face: huggingface.co/reflection
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Jacob Loewenstein retweeted
Reflection
@reflection_aiOct 16
Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source harness we built and trained Beam on: Mirror CLI Weights and model card on Hugging Face: huggingface.co/reflection
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Incredibly proud of @reflection_ai for immediately jumping into the #2 spot for US open models with their first ever model. Beam is SO fast and efficient for its size. Check it out
Artificial Analysis
@ArtificialAnlysOct 16
Reflection’s first model, Beam, scores 30 on the Artificial Analysis Intelligence Index, placing it #2 among US open weights models Beam is an open weights model with 501B total and 23B active parameters, released on @huggingface Congratulations to @reflection_ai on the release! Key takeaways: ➤ Second among US open weights models: Beam scores 30, behind only Meta's Muse Spark Open (39) and ahead of yesterday’s NVIDIA Nemotron 3.5 Super VL (26) release. Outside China, only Muse Spark Open and the UAE's K2 Horizon (31) score higher ➤ Token efficient for its intelligence: Beam uses ~26k output tokens per Intelligence Index task compared to K2 Horizon, one point ahead, which uses twice as many (~52k). Proprietary models still set the frontier, with GPT-6 Astra (max) scoring 53 on the same budget (~27k) ➤ Strong on agentic workflows, weaker in the terminal: Beam scores 48.5% on AutomationBench-AA, 11 percentage points ahead of K2 Horizon (37.2%) and level with Qwen3.8 27B (48.2%). Terminal-Bench 4.0 is a weak point, where Beam scores 3.0%, behind Qwen3.8 27B (5.6%) and Muse Spark Open (8.1%) ➤ Knows more, guesses more: On AA-Omniscience it answers 33% of questions correctly, well ahead of K2 Horizon (18%) and Qwen3.8 27B (16%). But its hallucination rate is 47%, against 26% for K2 Horizon and 30% for Qwen3.8 27B, giving Beam a net index score of 1.5, ahead of both peers (−3.0 and −10.0) Other model details Context window: 1M tokens Modalities: Text Parameters: 501B / 23B
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Jacob Loewenstein retweeted
Ian Andrews
@IanAndrewsDCOct 15
Nvidia model strategy: 1. Build great base models 2. OpenSource model, weights, training data & pipelines 3. Engage an ecosystem of partners for post training & domain or task specific fine tuning 4. Make available everywhere at great economics 5. Solve real customer problems 🎉
NVIDIA AI
@NVIDIAAIOct 15
Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active 🧵
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hive coding
Max Blade
@_MaxBladeOct 15
I have spent 3 weeks and two claude max accounts vibe coding a 50-person battle royale. Kong Royale. We invented hive coding, chats messages prompt 20 agents working overnight to fix and improve the game. and today we finally launch 🦍🍌 live playtest TODAY 1 PM ET. drop in with us 👇 youtube.com/watch?v=b5n1K_IA…
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Jacob Loewenstein retweeted
tae kim
@firstadopterOct 15
Nvidia CUDA creator Ian Buck is calling out Wall Street for failing to model the upside of MaxLPS.
tae kim
@firstadopterOct 15
An Interview with Nvidia’s Ian Buck on Why GPUs Keep Winning taekim.substack.com/p/an-int…
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2x the intelligence + multimodal input
Artificial Analysis
@ArtificialAnlysOct 15
Nvidia’s Nemotron 3.5 Super VL is now the leading US open weight model, scoring 26 on the Artificial Analysis Intelligence Index Nemotron 3.5 Super VL achieves twice the intelligence of its predecessor, Nemotron 3 Super (13), and does so using ~60% fewer output tokens per task It launches alongside Nemotron 3.5 Bolt 74B-A9B 24GB, the first model in its new Bolt family, which scores 21 on the Intelligence Index Congratulations to @NVIDIAAI on the releases! Key Takeaways: ➤ Leading US open weight model: Nemotron 3.5 Super VL scores 26.5, edging out Thinking Machines' Inkling Small (25.7) and ahead of NVIDIA's Nemotron 3 Ultra (23) and Meta's Muse Glimmer (17) ➤ Bolt comes within 2 points of Ultra: Nemotron 3.5 Bolt 74B-A9B 24GB scores 21, 1.5 points behind Nemotron 3 Ultra 550B A55B (23). The 2 bit memory has about a seventh of Ultra's parameters (74B vs 550B) and a sixth of its active parameters (9B vs 55B) ➤ Super 3.5 VL near lossless in NVFP4: Nemotron 3.5 Super VL releases in NVFP4, scoring within 0.1 points of its BF16 weights on the Intelligence Index, with no component moving by more than 2.6 points Nemotron 3.5 Super VL details Context window: 1M tokens Modalities: Vision and text input; text output Parameters: 120B / 12B Nemotron 3.5 Bolt 74B-A9B 24GB details Context window: 256k tokens Modalities: Vision and text input; text output Parameters: 74B / 9B
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Nemotron VL is here. Feed it all your images, videos, and documents 🍽️
NVIDIA AI
@NVIDIAAIOct 15
Meet NVIDIA Nemotron 3.5 Super VL Our new open model combines reasoning and tool use with text, image, document, and video understanding for long-running AI agents. 120B total parameters with 12B active 🧵
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Jacob Loewenstein retweeted
Together AI
@togethercomputeOct 15
NYC showed up! Together AI × NVIDIA × Reflection. A full room of builders, great conversations, and a shared excitement for what’s next in open AI. Thanks to everyone who joined us tonight. Berlin and San Francisco up next. This community keeps growing, and we’re just getting started. Find your city. Come build with us: luma.com/user/together_ai
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Jacob Loewenstein retweeted
Greg Burnham@GregHBurnhamOct 14
If you were transported back to 2015 with one instance of Opus 5.5 / Astra 6, but no foreknowledge, how rich could you get?
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Jacob Loewenstein retweeted
David
@DavidSHolzOct 11
Let's say a frontier model is a 10T MoE on GB300 NVL72. That's ~5 Joules/token or ~50 W at human thinking speed vs our brain at 20 W. The energy grid is $0.08/kWh and eating rice $0.50/kWh. So on an energy-per-thought standpoint rn we're surprisingly similar to frontier models!
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Jacob Loewenstein retweeted
Peter Zakin
@pzakinOct 11
getting older feels like my model is getting worse but harness is getting better.
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early lineup for this looks awesome 💪
ARC Prize
@arcprizeOct 9
Replying to @arcprize
Apply to attend: arcprize.org/events/research…

ARC Prize Research Summit 2026

A forum for researchers advancing frontier reasoning systems. October 23, 2026 in Boston.

arcprize.org
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Jacob Loewenstein retweeted
DailyPapers
@HuggingPapersOct 8
NVIDIA just released Long-WAM A world-action model that scales visual context for real-time robot control. Access to history is not the same as using it: longer histories pay off far more when the video foundation is pretrained autoregressively.
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Jacob Loewenstein retweeted
Michig ᠮᠢᠰᠾᠢᠭ@mishig25Oct 7
thanks to AI assisted math, bought the smallest table that can fit my home cluster of 11 mac minis using AI to do more AI
🤖 Made with AI
Square Packing Fan@ManassehA06Oct 6
The optimality of the packing for 11 squares has been formalized in lean thanks to Astra and Claude! Huge thanks to @ojoshe, @kleddamag, @wand_125, @guzhou0806, and @ctjlewis for aiding in the process. Image credit: jlevy.github.io/squares/case… 1/n
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Jacob Loewenstein retweeted
Lewis Tunstall
@_lewtunOct 7
This is the most important plot from the Beam release IMO. The Chinese models are great, but horribly token inefficient (try running an eval with max reasoning to feel the pain). I'm looking forward to a future where open models start competing on this axis!
Reflection
@reflection_aiOct 5
Replying to @reflection_ai
Beam leads in inference efficiency for its model class -- 3-4x more efficient than GLM 5.2 and over 4x more efficient than leading Western open models. This means Beam completes tasks faster and cheaper.
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Big things are coming in US open source. Monster evals for a model of this size 👹
Reflection
@reflection_aiOct 5
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: reflection.ai/beam
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